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docs/dl-nlp/SUMMARY.md
docs/dl-nlp/SUMMARY.md
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docs/dl-nlp/clean-text-machine-learning-python.md
docs/dl-nlp/clean-text-machine-learning-python.md
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docs/dl-nlp/define-encoder-decoder-sequence-sequence-model-neural-machine-translation-keras.md
...quence-sequence-model-neural-machine-translation-keras.md
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docs/dl-nlp/develop-a-caption-generation-model-in-keras.md
docs/dl-nlp/develop-a-caption-generation-model-in-keras.md
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-4
docs/dl-nlp/develop-word-based-neural-language-models-python-keras.md
...develop-word-based-neural-language-models-python-keras.md
+1
-1
docs/dl-nlp/develop-word-embeddings-python-gensim.md
docs/dl-nlp/develop-word-embeddings-python-gensim.md
+1
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docs/dl-nlp/encoder-decoder-deep-learning-models-text-summarization.md
...ncoder-decoder-deep-learning-models-text-summarization.md
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docs/dl-nlp/encoder-decoder-recurrent-neural-network-models-neural-machine-translation.md
...rrent-neural-network-models-neural-machine-translation.md
+1
-1
docs/dl-ts/instability-online-learning-stateful-lstm-time-series-forecasting.md
...-online-learning-stateful-lstm-time-series-forecasting.md
+1
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docs/dl-ts/lstm-model-architecture-for-rare-event-time-series-forecasting.md
...el-architecture-for-rare-event-time-series-forecasting.md
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docs/dl-ts/machine-learning-data-transforms-for-time-series-forecasting.md
...e-learning-data-transforms-for-time-series-forecasting.md
+1
-1
docs/linalg/computational-linear-algebra-coders-review.md
docs/linalg/computational-linear-algebra-coders-review.md
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docs/linalg/introduction-to-types-of-matrices-in-linear-algebra.md
...lg/introduction-to-types-of-matrices-in-linear-algebra.md
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docs/linalg/linear-algebra-cheat-sheet-for-machine-learning.md
...linalg/linear-algebra-cheat-sheet-for-machine-learning.md
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docs/linalg/linear-algebra-machine-learning-7-day-mini-course.md
...nalg/linear-algebra-machine-learning-7-day-mini-course.md
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docs/linalg/sparse-matrices-for-machine-learning.md
docs/linalg/sparse-matrices-for-machine-learning.md
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docs/lstm/develop-encoder-decoder-model-sequence-sequence-prediction-keras.md
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docs/lstm/how-to-use-an-encoder-decoder-lstm-to-echo-sequences-of-random-integers.md
...oder-decoder-lstm-to-echo-sequences-of-random-integers.md
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docs/ml-algo/bagging-and-random-forest-ensemble-algorithms-for-machine-learning.md
...random-forest-ensemble-algorithms-for-machine-learning.md
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docs/ml-algo/support-vector-machines-for-machine-learning.md
docs/ml-algo/support-vector-machines-for-machine-learning.md
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docs/sklearn/crash-course-python-machine-learning-developers.md
...klearn/crash-course-python-machine-learning-developers.md
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docs/sklearn/metrics-evaluate-machine-learning-algorithms-python.md
...rn/metrics-evaluate-machine-learning-algorithms-python.md
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docs/sklearn/python-ecosystem-machine-learning.md
docs/sklearn/python-ecosystem-machine-learning.md
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docs/stat/introduction-to-random-number-generators-for-machine-learning.md
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docs/ts-intro/python-environment-time-series-forecasting.md
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docs/dl-nlp/SUMMARY.md
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...
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@@ -11,7 +11,7 @@
+
[
自然语言处理的数据集
](
datasets-natural-language-processing.md
)
+
[
如何开发一种深度学习的词袋模型来预测电影评论情感
](
deep-learning-bag-of-words-model-sentiment-analysis.md
)
+
[
深度学习字幕生成模型的温和介绍
](
deep-learning-caption-generation-models.md
)
+
[
如何在 Keras 中定义神经机器翻译的编解码器序列
-
序列模型
](
define-encoder-decoder-sequence-sequence-model-neural-machine-translation-keras.md
)
+
[
如何在 Keras 中定义神经机器翻译的编解码器序列
到
序列模型
](
define-encoder-decoder-sequence-sequence-model-neural-machine-translation-keras.md
)
+
[
如何利用小实验在 Keras 中开发字幕生成模型
](
develop-a-caption-generation-model-in-keras.md
)
+
[
如何从头开发深度学习图片标题生成器
](
develop-a-deep-learning-caption-generation-model-in-python.md
)
+
[
如何在 Keras 中开发基于字符的神经语言模型
](
develop-character-based-neural-language-model-keras.md
)
...
...
docs/dl-nlp/clean-text-machine-learning-python.md
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# 如何用 Python
清理机器学习的
文本
# 如何用 Python
为机器学习清理
文本
> 原文: [https://machinelearningmastery.com/clean-text-machine-learning-python/](https://machinelearningmastery.com/clean-text-machine-learning-python/)
...
...
docs/dl-nlp/define-encoder-decoder-sequence-sequence-model-neural-machine-translation-keras.md
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# 如何在 Keras 中定义神经机器翻译的编解码器序列
-
序列模型
# 如何在 Keras 中定义神经机器翻译的编解码器序列
到
序列模型
> 原文: [https://machinelearningmastery.com/define-encoder-decoder-sequence-sequence-model-neural-machine-translation-keras/](https://machinelearningmastery.com/define-encoder-decoder-sequence-sequence-model-neural-machine-translation-keras/)
...
...
@@ -21,7 +21,7 @@
![
How to Define an Encoder-Decoder Sequence-to-Sequence Model for Neural Machine Translation in Keras
](
img/38cae6eb1536c9b0a1ba0bb9d1c6906e.jpg
)
如何在 Keras
中定义用于神经机器翻译的编解码器序列
-
序列模型
[
Tom Lee
](
https://www.flickr.com/photos/68942208@N02/16012752622/
)
,保留一些权利。
中定义用于神经机器翻译的编解码器序列
到
序列模型
[
Tom Lee
](
https://www.flickr.com/photos/68942208@N02/16012752622/
)
,保留一些权利。
## Keras 中的序列到序列预测
...
...
docs/dl-nlp/develop-a-caption-generation-model-in-keras.md
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# 如何利用小实验在 Keras 中开发
字幕
生成模型
# 如何利用小实验在 Keras 中开发
标题
生成模型
> 原文: [https://machinelearningmastery.com/develop-a-caption-generation-model-in-keras/](https://machinelearningmastery.com/develop-a-caption-generation-model-in-keras/)
字幕
生成是一个具有挑战性的人工智能问题,其中必须为照片生成文本描述。
标题
生成是一个具有挑战性的人工智能问题,其中必须为照片生成文本描述。
它既需要计算机视觉的方法来理解图像的内容,也需要来自自然语言处理领域的语言模型,以便将图像的理解转化为正确的单词。最近,深度学习方法已经在该问题的示例上获得了现有技术的结果。
在您自己的数据上开发
字幕
生成模型可能很困难,主要是因为数据集和模型太大而需要数天才能进行训练。另一种方法是使用较小数据集的小样本来探索模型配置。
在您自己的数据上开发
标题
生成模型可能很困难,主要是因为数据集和模型太大而需要数天才能进行训练。另一种方法是使用较小数据集的小样本来探索模型配置。
在本教程中,您将了解如何使用标准照片字幕数据集的小样本来探索不同的深度模型设计。
...
...
@@ -22,7 +22,7 @@
![
How to Use Small Experiments to Develop a Caption Generation Model in Keras
](
img/7d34b218f89d903c2711e5c2dc7e3027.jpg
)
如何使用小实验开发 Keras 中的
字幕
生成模型
如何使用小实验开发 Keras 中的
标题
生成模型
照片由
[
Per
](
https://www.flickr.com/photos/perry-pics/5968641588/
)
,保留一些权利。
## 教程概述
...
...
docs/dl-nlp/develop-word-based-neural-language-models-python-keras.md
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# 如何
在 Python 中用
Keras 开发基于单词的神经语言模型
# 如何
用 Python 和
Keras 开发基于单词的神经语言模型
> 原文: [https://machinelearningmastery.com/develop-word-based-neural-language-models-python-keras/](https://machinelearningmastery.com/develop-word-based-neural-language-models-python-keras/)
...
...
docs/dl-nlp/develop-word-embeddings-python-gensim.md
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# 如何使用
Gensim 在 Python 中
开发词嵌入
# 如何使用
Python 和 Gensim
开发词嵌入
> 原文: [https://machinelearningmastery.com/develop-word-embeddings-python-gensim/](https://machinelearningmastery.com/develop-word-embeddings-python-gensim/)
...
...
docs/dl-nlp/encoder-decoder-deep-learning-models-text-summarization.md
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#
使
用于文本摘要的编解码器深度学习模型
# 用于文本摘要的编解码器深度学习模型
> 原文: [https://machinelearningmastery.com/encoder-decoder-deep-learning-models-text-summarization/](https://machinelearningmastery.com/encoder-decoder-deep-learning-models-text-summarization/)
...
...
docs/dl-nlp/encoder-decoder-recurrent-neural-network-models-neural-machine-translation.md
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#
使
用于神经机器翻译的编解码器循环神经网络模型
# 用于神经机器翻译的编解码器循环神经网络模型
> 原文: [https://machinelearningmastery.com/encoder-decoder-recurrent-neural-network-models-neural-machine-translation/](https://machinelearningmastery.com/encoder-decoder-recurrent-neural-network-models-neural-machine-translation/)
...
...
docs/dl-ts/instability-online-learning-stateful-lstm-time-series-forecasting.md
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#
使
用于时间序列预测的状态 LSTM 在线学习的不稳定性
# 用于时间序列预测的状态 LSTM 在线学习的不稳定性
> 原文: [https://machinelearningmastery.com/instability-online-learning-stateful-lstm-time-series-forecasting/](https://machinelearningmastery.com/instability-online-learning-stateful-lstm-time-series-forecasting/)
...
...
docs/dl-ts/lstm-model-architecture-for-rare-event-time-series-forecasting.md
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#
使
用于罕见事件时间序列预测的 LSTM 模型体系结构
# 用于罕见事件时间序列预测的 LSTM 模型体系结构
> 原文: [https://machinelearningmastery.com/lstm-model-architecture-for-rare-event-time-series-forecasting/](https://machinelearningmastery.com/lstm-model-architecture-for-rare-event-time-series-forecasting/)
...
...
docs/dl-ts/machine-learning-data-transforms-for-time-series-forecasting.md
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#
使
用于时间序列预测的 4 种通用机器学习数据变换
# 用于时间序列预测的 4 种通用机器学习数据变换
> 原文: [https://machinelearningmastery.com/machine-learning-data-transforms-for-time-series-forecasting/](https://machinelearningmastery.com/machine-learning-data-transforms-for-time-series-forecasting/)
...
...
docs/linalg/computational-linear-algebra-coders-review.md
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#
使
用于编码器审查的计算线性代数
# 用于编码器审查的计算线性代数
> 原文: [https://machinelearningmastery.com/computational-linear-algebra-coders-review/](https://machinelearningmastery.com/computational-linear-algebra-coders-review/)
...
...
docs/linalg/introduction-to-types-of-matrices-in-linear-algebra.md
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#
使
用于机器学习的线性代数中的矩阵类型简介
# 用于机器学习的线性代数中的矩阵类型简介
> 原文: [https://machinelearningmastery.com/introduction-to-types-of-matrices-in-linear-algebra/](https://machinelearningmastery.com/introduction-to-types-of-matrices-in-linear-algebra/)
...
...
docs/linalg/linear-algebra-cheat-sheet-for-machine-learning.md
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#
使
用于机器学习的线性代数备忘单
# 用于机器学习的线性代数备忘单
> 原文: [https://machinelearningmastery.com/linear-algebra-cheat-sheet-for-machine-learning/](https://machinelearningmastery.com/linear-algebra-cheat-sheet-for-machine-learning/)
...
...
docs/linalg/linear-algebra-machine-learning-7-day-mini-course.md
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#
使
用于机器学习的线性代数(7 天迷你课程)
# 用于机器学习的线性代数(7 天迷你课程)
> 原文: [https://machinelearningmastery.com/linear-algebra-machine-learning-7-day-mini-course/](https://machinelearningmastery.com/linear-algebra-machine-learning-7-day-mini-course/)
...
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docs/linalg/sparse-matrices-for-machine-learning.md
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#
使
用于机器学习的稀疏矩阵的温和介绍
# 用于机器学习的稀疏矩阵的温和介绍
> 原文: [https://machinelearningmastery.com/sparse-matrices-for-machine-learning/](https://machinelearningmastery.com/sparse-matrices-for-machine-learning/)
...
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docs/lstm/develop-encoder-decoder-model-sequence-sequence-prediction-keras.md
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...
...
@@ -55,7 +55,7 @@ Keras深度学习Python库提供了一个如何实现机器翻译的编解码器
有关此型号的详细分类,请参阅帖子:
*
[
如何定义Keras神经机器翻译的编解码器序列
-
序列模型
](
https://machinelearningmastery.com/define-encoder-decoder-sequence-sequence-model-neural-machine-translation-keras/
)
*
[
如何定义Keras神经机器翻译的编解码器序列
到
序列模型
](
https://machinelearningmastery.com/define-encoder-decoder-sequence-sequence-model-neural-machine-translation-keras/
)
有关使用return_state的更多信息,可能是您的新手,请参阅帖子:
...
...
@@ -523,7 +523,7 @@ X=[50, 38, 17, 25, 31, 48] y=[17, 38, 50], yhat=[17, 38, 50]
### 相关文章
*
[
如何使用Anaconda设置用于机器学习和深度学习的Python环境
](
https://machinelearningmastery.com/setup-python-environment-machine-learning-deep-learning-anaconda/
)
*
[
如何定义Keras神经机器翻译的编解码器序列
-
序列模型
](
https://machinelearningmastery.com/define-encoder-decoder-sequence-sequence-model-neural-machine-translation-keras/
)
*
[
如何定义Keras神经机器翻译的编解码器序列
到
序列模型
](
https://machinelearningmastery.com/define-encoder-decoder-sequence-sequence-model-neural-machine-translation-keras/
)
*
[
了解Keras中LSTM的返回序列和返回状态之间的差异
](
https://machinelearningmastery.com/return-sequences-and-return-states-for-lstms-in-keras/
)
*
[
如何使用Keras功能API进行深度学习
](
https://machinelearningmastery.com/keras-functional-api-deep-learning/
)
...
...
docs/lstm/how-to-use-an-encoder-decoder-lstm-to-echo-sequences-of-random-integers.md
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...
...
@@ -250,7 +250,7 @@ for i in range(len(X)):
现在我们知道如何准备和表示整数的随机序列,我们可以看一下使用LSTM来学习它们。
## 回声全序列
(_序列
-
序列模型_)
(_序列
到
序列模型_)
在本节中,我们将开发一个LSTM,用于简单地解决问题,即预测或再现整个输入序列。
...
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docs/ml-algo/bagging-and-random-forest-ensemble-algorithms-for-machine-learning.md
浏览文件 @
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#
使
用于机器学习的装袋和随机森林集合算法
# 用于机器学习的装袋和随机森林集合算法
> 原文: [https://machinelearningmastery.com/bagging-and-random-forest-ensemble-algorithms-for-machine-learning/](https://machinelearningmastery.com/bagging-and-random-forest-ensemble-algorithms-for-machine-learning/)
...
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docs/ml-algo/support-vector-machines-for-machine-learning.md
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#
使
用于机器学习的支持向量机
# 用于机器学习的支持向量机
> 原文: [https://machinelearningmastery.com/support-vector-machines-for-machine-learning/](https://machinelearningmastery.com/support-vector-machines-for-machine-learning/)
...
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docs/sklearn/crash-course-python-machine-learning-developers.md
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#
使
用于机器学习开发人员的 Python 崩溃课程
# 用于机器学习开发人员的 Python 崩溃课程
> 原文: [https://machinelearningmastery.com/crash-course-python-machine-learning-developers/](https://machinelearningmastery.com/crash-course-python-machine-learning-developers/)
...
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docs/sklearn/metrics-evaluate-machine-learning-algorithms-python.md
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#
使
用于评估 Python 中机器学习算法的度量标准
# 用于评估 Python 中机器学习算法的度量标准
> 原文: [https://machinelearningmastery.com/metrics-evaluate-machine-learning-algorithms-python/](https://machinelearningmastery.com/metrics-evaluate-machine-learning-algorithms-python/)
...
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docs/sklearn/python-ecosystem-machine-learning.md
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#
使
用于机器学习的 Python 生态系统
# 用于机器学习的 Python 生态系统
> 原文: [https://machinelearningmastery.com/python-ecosystem-machine-learning/](https://machinelearningmastery.com/python-ecosystem-machine-learning/)
...
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docs/stat/introduction-to-random-number-generators-for-machine-learning.md
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#
使
用于Python机器学习的随机数生成器简介
# 用于Python机器学习的随机数生成器简介
> 原文: [https://machinelearningmastery.com/introduction-to-random-number-generators-for-machine-learning/](https://machinelearningmastery.com/introduction-to-random-number-generators-for-machine-learning/)
...
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docs/stat/statistics-in-plain-english-for-machine-learning.md
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#
使
用于机器学习的简明英语统计
# 用于机器学习的简明英语统计
> 原文: [https://machinelearningmastery.com/statistics-in-plain-english-for-machine-learning/](https://machinelearningmastery.com/statistics-in-plain-english-for-machine-learning/)
...
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docs/ts-intro/python-environment-time-series-forecasting.md
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#
使
用于时间序列预测的 Python 环境
# 用于时间序列预测的 Python 环境
> 原文: [https://machinelearningmastery.com/python-environment-time-series-forecasting/](https://machinelearningmastery.com/python-environment-time-series-forecasting/)
...
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编辑
预览
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